Federated Learning for Privacy-Preserving Autonomous Vehicle Data Analysis
Keywords:
raining machine learning modelsAbstract
Moreover, the raw data that can be sensed, stored, and processed by autonomous vehicles are most likely to contain a lot of private attributes. Many of the personal behaviors such as long-term visiting habits, individual driving style, and demographic characteristics can be inferred from sensor data from autonomous vehicles. Potential adversaries to privacy can also exploit the vehicle data to recognize individuals, monitor their behaviors, and even damage their reputation [1]. Consequently, it is important to develop a mechanism that preserves the data privacy of the autonomous vehicles, as well as the privacy of inferred attributes by training machine learning models using the data.
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References
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